Published on April 11, 2024, 7:17 am

Stay updated on the latest developments in Generative AI with CIO Dive’s free newsletter delivered straight to your inbox. According to Gartner analysts, generative AI technology is currently positioned at the beginning stages of its hype cycle, indicating substantial room for growth ahead.

Enterprises are optimistic about the transformative potential of generative AI across various business sectors including operations and user experience. Recent data from a Gartner survey conducted in September revealed that over half of organizations are either piloting or actively using generative AI, marking a significant 36 percentage point increase from just last April.

One of the key questions surrounding generative AI is whether it can fulfill its promise. Organizations are predominantly focusing on natural language processing tasks within their generative AI projects; these tasks include summarization, language translation, and sentiment analysis as highlighted by Gartner’s research.

Conversational chatbots utilizing Large Language Models (LLMs) have emerged as a popular starting point for organizations venturing into generative AI exploration. Major companies like PwC, Deloitte, EY, McKinsey, General Mills, Kraft Heinz, Procter & Gamble, Walmart, and American Honda have integrated internal chatbots to aid employees in information retrieval and document summarization.

While deploying conversational tools represents a crucial aspect of an organization’s broader AI strategy, tech leaders are increasingly looking towards integrating generative AI for supporting IT operations via code completion and documentation tools. Gartner predicts that mainstream adoption of AI-augmented software engineering will gather momentum within the next two to five years.

Despite the rapid advancements in models within this realm, issues persist around enterprise data engineering and quality standards lagging behind technological progress. Chandrasekaran emphasizes the need for investing upfront in updating tech infrastructure to maximize the utility of generative AI tools for large enterprises.

Data plays a pivotal role in enabling effective usage of generative AI models and tools within organizations. Leaders must prioritize upgrading their tech stack to align with overarching goals for tangible value generation from these technologies.

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